Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction
High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-01-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/19942060.2020.1758792 |
| _version_ | 1818920768697794560 |
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| author | Ellysia Jumin Nuratiah Zaini Ali Najah Ahmed Samsuri Abdullah Marzuki Ismail Mohsen Sherif Ahmed Sefelnasr Ahmed El-Shafie |
| author_facet | Ellysia Jumin Nuratiah Zaini Ali Najah Ahmed Samsuri Abdullah Marzuki Ismail Mohsen Sherif Ahmed Sefelnasr Ahmed El-Shafie |
| author_sort | Ellysia Jumin |
| collection | DOAJ |
| description | High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R2 values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia. |
| first_indexed | 2024-12-20T01:27:00Z |
| format | Article |
| id | doaj.art-06312a6c996d49d8b8b254768ec80021 |
| institution | Directory Open Access Journal |
| issn | 1994-2060 1997-003X |
| language | English |
| last_indexed | 2024-12-20T01:27:00Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj.art-06312a6c996d49d8b8b254768ec800212022-12-21T19:58:13ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2020-01-0114171372510.1080/19942060.2020.17587921758792Machine learning versus linear regression modelling approach for accurate ozone concentrations predictionEllysia Jumin0Nuratiah Zaini1Ali Najah Ahmed2Samsuri Abdullah3Marzuki Ismail4Mohsen Sherif5Ahmed Sefelnasr6Ahmed El-Shafie7Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN)Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN)Institute for Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN)Air Quality and Environment Research Group, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia TerengganuFaculty of Science and Marine Environment, Universiti Malaysia TerengganuNational Water Center (NWC), United Arab Emirates UniversityNational Water Center (NWC), United Arab Emirates UniversityNational Water Center (NWC), United Arab Emirates UniversityHigh level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R2 values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia.http://dx.doi.org/10.1080/19942060.2020.1758792ozone concentration predictionmachine learning algorithmozone precursorsboosted decision tree regressionneural networklinear regressionpearson correlation |
| spellingShingle | Ellysia Jumin Nuratiah Zaini Ali Najah Ahmed Samsuri Abdullah Marzuki Ismail Mohsen Sherif Ahmed Sefelnasr Ahmed El-Shafie Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction Engineering Applications of Computational Fluid Mechanics ozone concentration prediction machine learning algorithm ozone precursors boosted decision tree regression neural network linear regression pearson correlation |
| title | Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction |
| title_full | Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction |
| title_fullStr | Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction |
| title_full_unstemmed | Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction |
| title_short | Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction |
| title_sort | machine learning versus linear regression modelling approach for accurate ozone concentrations prediction |
| topic | ozone concentration prediction machine learning algorithm ozone precursors boosted decision tree regression neural network linear regression pearson correlation |
| url | http://dx.doi.org/10.1080/19942060.2020.1758792 |
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